Risky Business: Meeting the Structural Needs of Transdisciplinary Science

Wise, Paul H.; Shaw, Gary M.; Druzin, Maurice L.; Quaintance, Cecele C.; Mäkinen, Elina; Relman, David A.; Quake, Stephen R.; Butte, Atul J.; Angst, Martin S.; Muglia, Louis J.; Macones, George A.; Driscoll, Deborah A.; Ober, Carole; Simpson, Joe Leigh; Katz, Michael; Howse, Jennifer L.; Stevenson, David K.

doi:10.1016/j.jpeds.2017.08.072

Cited by 11 publications

(7 citation statements)

References 20 publications

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“…To foster a new model of collaboration with the hope of leading to transformative discoveries, six MOD Prematurity Research Centers have been launched. The goals are to integrate scientists from individual disciplines and to form innovative collaborations that can accelerate research discoveries 20 . The first Center was launched in 2011 at Stanford University School of Medicine to study infection and inflammation, transcriptomics and lead bioinformatics effort in PTB research.…”

Section: Background and Summarymentioning

confidence: 99%

Enabling precision medicine in neonatology, an integrated repository for preterm birth research

et al. 2018

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Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a significant cause of infant morbidity and mortality. In the last decade, the advent and continued development of molecular profiling technologies has enabled researchers to generate vast amount of ‘omics’ data, which together with integrative computational approaches, can help refine the current knowledge about disease mechanisms, diagnostics, and therapeutics. Here we describe the March of Dimes’ Database for Preterm Birth Research (http://www.immport.org/resources/mod), a unique resource that contains a variety of ‘omics’ datasets related to preterm birth. The database is open publicly, and as of January 2018, links 13 molecular studies with data across tens of thousands of patients from 6 measurement modalities. The data in the repository are highly diverse and include genomic, transcriptomic, immunological, and microbiome data. Relevant datasets are augmented with additional molecular characterizations of almost 25,000 biological samples from public databases. We believe our data-sharing efforts will lead to enhanced research collaborations and coordination accelerating the overall pace of discovery in preterm birth research.

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Section: Background and Summarymentioning

confidence: 99%

Enabling precision medicine in neonatology, an integrated repository for preterm birth research

et al. 2018

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“…Pregnant women participating in a cohort study sponsored by the March of Dimes Prematurity Research Center were prospectively examined for an array of environmental and biological factors associated with uncomplicated and pathological pregnancies (27, 28). Participants all received routine antepartum care at the Lucile Packard Children's Hospital at Stanford University and were eligible for the study if they were 18 years of age or older and in their first trimester of pregnancy.…”

Section: Methodsmentioning

confidence: 99%

Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia

et al. 2019

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Preeclampsia is one of the most severe pregnancy complications and a leading cause of maternal death. However, early diagnosis of preeclampsia remains a clinical challenge. Alterations in the normal immune adaptations necessary for the maintenance of a healthy pregnancy are central features of preeclampsia. However, prior analyses primarily focused on the static assessment of select immune cell subsets have provided limited information for the prediction of preeclampsia. Here, we used a high-dimensional mass cytometry immunoassay to characterize the dynamic changes of over 370 immune cell features (including cell distribution and functional responses) in maternal blood during healthy and preeclamptic pregnancies. We found a set of eight cell-specific immune features that accurately identified patients well before the clinical diagnosis of preeclampsia (median area under the curve (AUC) 0.91, interquartile range [0.82–0.92]). Several features recapitulated previously known immune dysfunctions in preeclampsia, such as elevated pro-inflammatory innate immune responses early in pregnancy and impaired regulatory T (Treg) cell signaling. The analysis revealed additional novel immune responses that were strongly associated with, and preceded the onset of preeclampsia, notably abnormal STAT5ab signaling dynamics in CD4 + T cell subsets (AUC 0.92, p = 8.0E-5). These results provide a global readout of the dynamics of the maternal immune system early in pregnancy and lay the groundwork for identifying clinically-relevant immune dysfunctions for the prediction and prevention of preeclampsia.

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“…Preterm birth is the major cause of neonatal death, and the second leading cause of mortality in children under the age of 5 years (Liu et al , 2012). An ongoing cohort study by the March of Dimes Prematurity Research Center at Stanford University exploits recent technological advances to examine an array of biological, demographic, clinical and environmental factors associated with normal and pathological pregnancies (Stevenson et al , 2013; Shaw et al , 2018; Wise et al , 2017). From a biological perspective, this effort has so far produced two major lines of evidence.…”

Section: Introductionmentioning

confidence: 99%

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

et al. 2018

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Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia. Results: We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified. Availability and implementation: Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.

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Risky Business: Meeting the Structural Needs of Transdisciplinary Science

Cited by 11 publications

References 20 publications

Enabling precision medicine in neonatology, an integrated repository for preterm birth research

Enabling precision medicine in neonatology, an integrated repository for preterm birth research

Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia

Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy

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